Sentence Similarity
Transformers
PyTorch
Safetensors
sentence-transformers
English
Russian
xlm-roberta
feature-extraction
mteb
retrieval
retriever
pruned
e5
text-embeddings-inference
Instructions to use d0rj/e5-base-en-ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use d0rj/e5-base-en-ru with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("d0rj/e5-base-en-ru") model = AutoModel.from_pretrained("d0rj/e5-base-en-ru", device_map="auto") - sentence-transformers
How to use d0rj/e5-base-en-ru with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("d0rj/e5-base-en-ru") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download sentence_bert_config.json from d0rj/e5-base-en-ru: direct link, hf CLI and curl.
- Browser
- Download file 53 Bytes
-
https://huggingface.co/d0rj/e5-base-en-ru/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://d0rj/e5-base-en-ru/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/d0rj/e5-base-en-ru/resolve/main/sentence_bert_config.json
53 Bytes
| { | |
| "max_seq_length": 514, | |
| "do_lower_case": false | |
| } |